Investigation of Inaccuracies in the Assessment of Ventricular Late Potentials Using 12-lead ECG Recordings

Introduction. Ventricular late potentials (VLP) are predictors of cardiac disorders such as sudden death syndrome, myocardial infarction and ventricular tachyarrhythmias. Therefore, VLP assessment allows the severity and possible dangerous consequences of such disorders to be predicted.Aim. To determine errors associated with VLP assessment by high-resolution 12-lead ECG recordings.Materials and methods. VLPs were determined by the modulus of the cardiac electrical vector using signals from orthogonal leads. The conversion error was assessed using synchronous ECG recordings of 12-channel and orthogonal leads, the method of digital filtering (to reduce noise and interference) and the method of identifying characteristic points of the QRS complex and VLPs.Results. The conversion of 12-lead ECG signals into orthogonal signals results in errors associated with the assessment of both the modulus of the cardiac electrical vector and all VLP indicators. The Kors transformation was shown to provide the minimum errors when assessing the cardiac electrical vector modulus in the QRS area, with the errors related to the VRMS assessment not exceeding 0.084 %. The estimation of the QRSd and LAS errors should consider the nature of VLP variations and the zone of uncertainty in their assessment. The ambiguity of the results of assessing the boundaries of violations and the absence of pathologies in cardiac ventricular depolarization indicates the influence of a large number of factors on research accuracy. Errors in the assessment of these factors may result in under- and overestimation of dangerous heart rhythm disturbances and incorrect prediction of the patient' state.Conclusion. The obtained results can be used for reducing errors associated with the assessment of VLP indicators, improving the diagnostic accuracy of dangerous heart rhythm disturbances and predicting disease exacerbation due to structural and morphological disorders of the myocardium.

Ventricular late potentials measured by signal-averaged electrocardiogram in young professional soccer players

Background and objectives Athlete’s heart is characterized by structural cardiac changes, including enlargement and hypertrophy. However, exercise-induced cardiac electrical remodeling is not well known in Asian athletes. We sought to evaluate the association between vigorous exercise and the development of abnormal late potential on signal-averaged electrocardiogram (SAECG). Method We analyzed 48 Korean professional soccer players and 71 healthy sedentary controls who underwent SAECG and transthoracic echocardiography at Kyung Hee University Hospital. An SAECG was considered abnormal (positive for ventricular late potential) when any one of the three following criteria was met: filtered QRS duration > 114 ms, root-mean-square voltage in the terminal 40 ms < 20 uV, or a voltage < 40 uV for more than 38 ms. Results Fragmented QRS was more commonly found in athletes (1.4% vs. 10.4%). Athletes demonstrated significantly higher proportion of filtered QRS duration > 114 ms (7.0% vs. 22.9%, P = 0.013) and lower terminal QRS root-mean-square voltage < 20 uV (5.6% vs. 20.8%, P = 0.012). Ventricular late potential on SAECG was significantly more frequent in athletes (15.5% vs. 35.4%, P = 0.012). Regarding echocardiographic parameters, the athletes had larger cardiac chamber size; however, these differences became non-significant after adjustment for body surface area, except left ventricular mass index (65.7 ± 12.7 g/m2 vs. 84.7 ± 17.7 g/m2, P < 0.001). Conclusion Abnormal SAECG findings were significantly more common in athletes than in controls. Further study is needed to determine the clinical impact of these abnormal SAECGs in athletes and cardiac outcomes in the long term.

Efficient Detection of Ventricular Late Potentials on ECG Signals Based on Wavelet Denoising and SVM Classification

The analysis of cardiac signals is still regarded as attractive by both the academic community and industry because it helps physicians in detecting abnormalities and improving the diagnosis and therapy of diseases. Electrocardiographic signal processing for detecting irregularities related to the occurrence of low-amplitude waveforms inside the cardiac signal has a considerable workload as cardiac signals are heavily contaminated by noise and other artifacts. This paper presents an effective approach for the detection of ventricular late potential occurrences which are considered as markers of sudden cardiac death risk. Three stages characterize the implemented method which performs a beat-to-beat processing of high-resolution electrocardiograms (HR-ECG). Fifteen lead HR-ECG signals are filtered and denoised for the improvement of signal-to-noise ratio. Five features were then extracted and used as inputs of a classifier based on a machine learning approach. For the performance evaluation of the proposed method, a HR-ECG database consisting of real ventricular late potential (VLP)-negative and semi-simulated VLP-positive patterns was used. Experimental results show that the implemented system reaches satisfactory performance in terms of sensitivity, specificity accuracy, and positive predictivity; in fact, the respective values equal to 98.33%, 98.36%, 98.35%, and 98.52% were achieved.

Marginal Component Analysis of ECG Signals for Beat-to-Beat Detection of Ventricular Late Potentials

Heart condition diagnosis based on electrocardiogram signal analysis is the basic method used in prevention of cardiovascular diseases, which are recognized as the leading cause of death globally. To anticipate the occurrence of ventricular arrhythmia, the detection of Ventricular Late Potentials (VLPs) is clinically worthwhile. VLPs are low-amplitude and high-frequency signals appearing at the end part of QRS complexes in the electrocardiogram, which can be considered as a robust feature for arrhythmia risk stratification in patients with cardiac diseases. This paper proposes a beat-to-beat VLP detection method based on the the marginal component analysis and investigates its performance taking into account different ratios between QRS and VLP power. After a denoising phase, performed adopting the singular vector decomposition technique, heartbeats characterized by VLP onsets are identified and extracted taking into account the vector magnitude of each high resolution ECG (HR-ECG) record. To evaluate the proposed method performance, a 15-lead HR-ECG database consisting of real VLP-negative and simulated VLP-positive patterns was used. The achieved results highlight the method validity for VLP detection.

Right ventricular free-wall scar: an exceptional source of post-infarction ventricular tachycardia. A case report

Background In patients with coronary artery disease, ventricular tachycardia (VT) is usually related to left ventricular (LV) post-infarction scars. Case summary A case of a 78-year-old man with post-infarction VT originating from the right ventricular (RV) free wall is described. Following recurrent episodes of VT with left bundle branch block morphology and left superior axis deviation, a patient with prior myocardial infarction was submitted to catheter ablation. Two areas of abnormal bipolar electrograms were observed at 3D electroanatomical mapping: one located at the basal aspect of the posterior and postero-septal LV, and the other one extending from the antero-lateral to the posterior mid-basal RV free wall. Ventricular late potentials (LPs) were recorded within both scars, but only pacing from those located in the RV resulted in long stimulus-to-QRS latency and optimal pace-mapping. Accordingly, this substrate was deemed the culprit of the clinical VT. Radiofrequency catheter ablation aimed at eliminating all LPs recorded from both scars was effective in preventing VT recurrences at follow-up. Discussion A post-infarction RV free-wall scar may exceptionally be responsible of VT occurrence. Right ventricular mapping should be considered in selected cases based on 12-lead electrocardiogram VT morphology and prior RV infarct.